Instructions to use voconly-org/Qwen3.5-9B-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use voconly-org/Qwen3.5-9B-Q4_K_M-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf voconly-org/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf voconly-org/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf voconly-org/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf voconly-org/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf voconly-org/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf voconly-org/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf voconly-org/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf voconly-org/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/voconly-org/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use voconly-org/Qwen3.5-9B-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/voconly-org/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M
```
Unsloth Desktop

How to use voconly-org/Qwen3.5-9B-Q4_K_M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf voconly-org/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "voconly-org/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use voconly-org/Qwen3.5-9B-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/voconly-org/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use voconly-org/Qwen3.5-9B-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull voconly-org/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-9B-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use voconly-org/Qwen3.5-9B-Q4_K_M-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf voconly-org/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default voconly-org/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use voconly-org/Qwen3.5-9B-Q4_K_M-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf voconly-org/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "voconly-org/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Qwen3.5-9B-Q4_K_M-GGUF

Model Introduction

This is the GGUF format quantized version of Qwen3.5-9B, suitable for local deployment and inference using llama.cpp and compatible frameworks.

Original Model: Qwen/Qwen3.5-9B

Quantization Information

Quantization Method: Q4_K_M (4-bit Medium Quality)
File Size: ~5.3 GB
Quantization Quality: Balanced between model size and performance, suitable for most use cases

Q4_K_M uses 4-bit quantization for most weights while keeping important layers in higher precision, offering a good trade-off between speed and quality.

Usage

llama.cpp Command Line

# Interactive chat
./llama-cli -m Qwen3.5-9B-Q4_K_M.gguf -ngl 40 -c 8192 --chat-template qwen

# Server mode
./llama-server -m Qwen3.5-9B-Q4_K_M.gguf -ngl 40 -c 8192 --port 8080

Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(
    model_path="Qwen3.5-9B-Q4_K_M.gguf",
    n_gpu_layers=40,
    n_ctx=8192,
)

response = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Hello, how are you?"}]
)
print(response["choices"][0]["message"]["content"])

Hardware Requirements

Configuration	Minimum RAM	Recommended RAM
CPU-only	12 GB	16 GB
GPU (NVIDIA)	8 GB VRAM	10 GB VRAM

Recommended Hardware:

GPU: NVIDIA RTX 3080 (10GB) or RTX 4070 Ti (12GB) or higher
CPU: Modern multi-core processor with AVX2 support
RAM: 16GB+ system memory

Download Links

ModelScope: Voconly/Qwen3.5-9B-Q4_K_M-GGUF
HuggingFace: Voconly/Qwen3.5-9B-Q4_K_M-GGUF

License

This model follows the original Qwen3.5 license. Please refer to the original model's license for usage terms.

Downloads last month: 90

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

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